Chand Tara, Vaishanavaa Pankaj, Dubey Ashwini Kumar, Misra Gauri
National Institute of Biologicals, Ministry of Health and Family Welfare, Government of India, A-32, Sector-62, Noida, Uttar Pradesh 201309, India; Academy of Scientific and Innovative Research (AcSIR), CSIR-Human Resource Development Centre (CSIR-HRDC) Campus, Ghaziabad, Uttar Pradesh 201002, India.
National Institute of Biologicals, Ministry of Health and Family Welfare, Government of India, A-32, Sector-62, Noida, Uttar Pradesh 201309, India.
Comput Biol Chem. 2025 Dec;119:108605. doi: 10.1016/j.compbiolchem.2025.108605. Epub 2025 Aug 5.
Cervical cancer remains a prevalent malignancy with rising incidence, primarily due to sexual transmission, persistent HPV infection, and delayed screening. Identifying new biomarkers is critical for improved diagnosis, prognosis, and treatment of cervical cancer. This study utilized integrated bioinformatics to identify potential biomarkers by analysing gene expression data from the GEO database.
Four GEO microarray datasets (GSE7410, GSE7803, GSE52903, GSE67522) were analysed using GEO2R to identify DEGs with an adjusted p-value <0.05. Common DEGs were visualized using Venn diagrams. Protein-protein interaction network was constructed using STRING to identify hub genes. Gene Ontology (GO) and KEGG pathway analyses were performed to investigate biological functions and pathways. The Human Protein Atlas (HPA) was used for in silico validation of protein expression via immunohistochemistry. Kaplan-Meier survival analysis was performed to determine the prognostic value of hub genes.
Analysis revealed 684 common DEGs across the datasets (446 upregulated, 238 downregulated). The top 20 upregulated DEGs from GSE67522 were used for heatmap construction and PPI analysis, leading to the identification of nine key hub genes. GO and KEGG analyses showed that six of these were significantly involved in cell cycle regulation and tumorigenic pathways. These hub genes were validated for their protein expression through HPA data.
Six hub genes (CCNB2, AURKA, CDC20, CDT1, CENPF, and KIF2C) were identified as potential biomarkers for cervical cancer management.
These findings provide valuable insight into the molecular profiles of genes that play significant roles in cervical cancer for translational outcomes in diagnosis.
宫颈癌仍然是一种发病率不断上升的常见恶性肿瘤，主要原因是性传播、持续性人乳头瘤病毒（HPV）感染以及筛查延迟。识别新的生物标志物对于改善宫颈癌的诊断、预后和治疗至关重要。本研究利用综合生物信息学方法，通过分析基因表达综合数据库（GEO）中的基因表达数据来识别潜在的生物标志物。
使用GEO2R分析四个GEO微阵列数据集（GSE7410、GSE7803、GSE52903、GSE67522），以识别校正后p值<0.05的差异表达基因（DEG）。使用韦恩图对常见的DEG进行可视化分析。利用STRING构建蛋白质-蛋白质相互作用网络以识别枢纽基因。进行基因本体（GO）和京都基因与基因组百科全书（KEGG）通路分析，以研究生物学功能和通路。利用人类蛋白质图谱（HPA）通过免疫组织化学对蛋白质表达进行计算机验证。进行Kaplan-Meier生存分析以确定枢纽基因的预后价值。
分析发现各数据集中共有684个常见DEG（446个上调，238个下调）。来自GSE67522的前20个上调DEG用于构建热图和蛋白质-蛋白质相互作用分析，从而识别出9个关键枢纽基因。GO和KEGG分析表明，其中6个基因显著参与细胞周期调控和肿瘤发生通路。通过HPA数据对这些枢纽基因的蛋白质表达进行了验证。
六个枢纽基因（细胞周期蛋白B2、极光激酶A、细胞分裂周期蛋白20、染色体复制许可因子1、着丝粒蛋白F和驱动蛋白家族成员2C）被确定为宫颈癌管理的潜在生物标志物。
这些发现为在宫颈癌中发挥重要作用的基因的分子特征提供了有价值的见解，有助于实现诊断方面的转化成果。
